Modeling obesity using abductive networks.
This paper investigates the use of abductive-network machine learning for modeling and predicting outcome parameters in terms of input parameters in medical survey data. Here we consider modeling obesity as represented by the waist-to-hip ratio (WHR) risk factor to investigate the influence of various parameters. The same approach would be useful in predicting values of clinical parameters that are difficult or expensive to measure from others that are more readily available. The AIM abductive network machine learning tool was used to model the WHR from 13 other health parameters. Survey data were collected for a randomly selected sample of 1100 persons aged 20 yr and over attending nine primary health care centers at Al-Khobar, Saudi Arabia. Models were synthesized by training on a randomly selected set of 800 cases, using both continuous and categorical representations of the parameters, and evaluated by predicting the WHR value for the remaining 300 cases. Models for WHR as a continuous variable predict the actual values within an error of 7.5% at the 90% confidence limits. Categorical models predict the correct logical value of WHR with an error in only 2 of the 300 evaluation cases. Analytical relationships derived from simple categorical models explain global observations on the total survey population to an accuracy as high as 99%. Simple continuous models represented as analytical functions highlight global relationships and trends. Results confirm the strong correlation between WHR and diastolic blood pressure, cholesterol level, and family history of obesity. Compared to other statistical and neural network approaches, AIM abductive networks provide faster and more automated model synthesis. A review is given of other areas where the proposed modeling approach can be useful in clinical practice.
Abdel-Aal
R E
RE
Research Institute, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Mangoud
A M
AM
D000328:Adult; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D001794:Blood Pressure; D001824:Body Constitution; D015992:Body Mass Index; D001835:Body Weight; D002784:Cholesterol / Q000097:blood; D016001:Confidence Intervals; D003971:Diastole; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008954:Models, Biological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009765:Obesity / Q000209:etiology* / Q000235:genetics; D017063:Outcome Assessment, Health Care; D012307:Risk Factors; D012529:Saudi Arabia; D014280:Triglycerides / Q000097:blood
D001786:Blood Glucose; D014280:Triglycerides; D002784:Cholesterol